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Mendeley readers
Attention Score in Context
| Title |
Incorporation of subject-level covariates in quantile normalization of miRNA data
|
|---|---|
| Published in |
BMC Genomics, December 2015
|
| DOI | 10.1186/s12864-015-2199-4 |
| Pubmed ID | |
| Authors |
Anvar Suyundikov, John R. Stevens, Christopher Corcoran, Jennifer Herrick, Roger K. Wolff, Martha L. Slattery |
| Abstract |
Most currently-used normalization methods for miRNA array data are based on methods developed for mRNA arrays despite fundamental differences between the data characteristics. The application of conventional quantile normalization can mask important expression differences by ignoring demographic and environmental factors. We present a generalization of the conventional quantile normalization method, making use of available subject-level covariates in a colorectal cancer study. In simulation, our weighted quantile normalization method is shown to increase statistical power by as much as 10 % when relevant subject-level covariates are available. In application to the colorectal cancer study, this increase in power is also observed, and previously-reported dysregulated miRNAs are rediscovered. When any subject-level covariates are available, the weighted quantile normalization method should be used over the conventional quantile normalization method. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 9 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 4 | 44% |
| Student > Master | 2 | 22% |
| Professor | 1 | 11% |
| Lecturer | 1 | 11% |
| Student > Ph. D. Student | 1 | 11% |
| Other | 0 | 0% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 44% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 11% |
| Nursing and Health Professions | 1 | 11% |
| Agricultural and Biological Sciences | 1 | 11% |
| Medicine and Dentistry | 1 | 11% |
| Other | 1 | 11% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 December 2015.
All research outputs
#18,432,465
of 22,835,198 outputs
Outputs from BMC Genomics
#8,183
of 10,655 outputs
Outputs of similar age
#280,827
of 389,038 outputs
Outputs of similar age from BMC Genomics
#306
of 342 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% of its peers scored the same or lower than it.
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We're also able to compare this research output to 342 others from the same source and published within six weeks on either side of this one. This one is in the 5th percentile – i.e., 5% of its contemporaries scored the same or lower than it.